1. Statement of the Technical Field
The inventive arrangements relate to methods and systems for object identification and pose detection based on three-dimensional point cloud data, and more particular methods for such object identification and pose detection which are based on a combination of techniques involving analysis in the spatial domain and in the frequency domain.
2. Description of the Related Art
Three dimensional (3D) sensing systems, such as LiDAR, can generate 3D image data. For example, LiDAR systems operate by recording multiple range echoes from pulses of laser light to generate an image frame. Such data is comprised of a collection of points in three dimensional space which correspond to the multiple range echoes within a sensor aperture. The data recorded by a 3D sensing system is sometimes referred to as a three dimensional point cloud data and the data points in a 3D point cloud data are sometimes referred to as “voxels.” Each voxel can represent a data point value on a regular grid in three dimensional space. In this regard, each data point in the 3D point cloud typically has an individual x, y and z value, such that the point cloud can represent an actual surface within a scene in 3D. Each point can also have an intensity value. With the foregoing information, LiDAR data can be processed to reconstruct a three-dimensional representation of a surface or terrain.
3D point cloud data can be particularly useful when the point cloud data facilitates identification of specific objects within a scene. However, the ability to consistently and accurately identify specific objects in a collection of 3D point cloud data is a complex problem and this capability is not available in conventional systems. Similarly, the ability to consistently and accurately identify the pose of a particular object is a complex problem which has not been reliably solved using current techniques. The difficulty of these object identification and pose detection tasks is increased by the absence of important data points, and/or the presence of spurious data.